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Peer-reviewedCC BY-NC-NDThe American Journal of Human Genetics

DiscoDivas: Leveraging genetic-ancestry continuum information to interpolate PRS for admixed populations

  • Yunfeng Ruan1
  • Rohan Bhukar1
  • Aniruddh P. Patel2
  • Satoshi Koyama
  • Leland E. Hull3
  • Buu Minh Thanh Truong
  • So Mi Jemma Cho4
  • Whitney Hornsby1
  • Haoyu Zhang5
  • Nilanjan Chatterjee6
  • Pradeep Natarajan
6 affiliations
  1. 1Program in Medical and Population, Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
  2. 2Program in Medical and Population, Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
  3. 3Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
  4. 4Program in Medical and Population, Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea
  5. 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
  6. 6Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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Abstract

Genome-wide association study (GWAS) summary statistics for training and individual-level cohorts for fine-tuning are essential for constructing predictive polygenic risk score (PRS) models. However, the relatively low representation of admixed populations in both GWAS summary statistics and individual-level datasets hinders the development of PRSs and equitable clinical translation for admixed populations. Prior work indicates that the most informative PRS model for a genetically homogeneous sample varies linearly in an ancestry continuum space. Guided by these observations, we introduce a genetic-distance-assisted PRS combination pipeline for diverse genetic ancestries (DiscoDivas) to interpolate a harmonized PRS for diverse, especially admixed, genetic ancestries. DiscoDivas leverages multiple PRS models fine-tuned within existing samples, which are mostly of single ancestry, and genetic distance. It provides a new approach to generate genetic-ancestry-specific PRSs when a suitably matched individual-level fine-tuning cohort is unavailable or underpowered. DiscoDivas treats genetic ancestry as a continuous variable and does not require shifting across different models when calculating PRSs for different ancestries. We generated PRSs with DiscoDivas and the current conventional method, i.e., fine-tuning multiple GWAS PRSs using the matched or similar genetic-ancestry samples. DiscoDivas generated a harmonized PRS, performing comparable to or better than the conventional approach, with the greatest advantage exhibited in admixed individuals.

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Cite

BibTeXRIS
BibTeX
@article{ruan2026discodivas,
  title = {{DiscoDivas: Leveraging genetic-ancestry continuum information to interpolate PRS for admixed populations}},
  author = {Yunfeng Ruan and Rohan Bhukar and Aniruddh P. Patel and Satoshi Koyama and Leland E. Hull and Buu Minh Thanh Truong and So Mi Jemma Cho and Whitney Hornsby and Haoyu Zhang and Nilanjan Chatterjee and Pradeep Natarajan},
  journal = {The American Journal of Human Genetics},
  year = {2026},
  month = jun,
  doi = {10.1016/j.ajhg.2026.05.006},
  url = {https://doi.org/10.1016/j.ajhg.2026.05.006},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - DiscoDivas: Leveraging genetic-ancestry continuum information to interpolate PRS for admixed populations
AU  - Yunfeng Ruan
AU  - Rohan Bhukar
AU  - Aniruddh P. Patel
AU  - Satoshi Koyama
AU  - Leland E. Hull
AU  - Buu Minh Thanh Truong
AU  - So Mi Jemma Cho
AU  - Whitney Hornsby
AU  - Haoyu Zhang
AU  - Nilanjan Chatterjee
AU  - Pradeep Natarajan
PY  - 2026
DA  - 2026/06/03
JO  - The American Journal of Human Genetics
DO  - 10.1016/j.ajhg.2026.05.006
UR  - https://doi.org/10.1016/j.ajhg.2026.05.006
AB  - Genome-wide association study (GWAS) summary statistics for training and individual-level cohorts for fine-tuning are essential for constructing predictive polygenic risk score (PRS) models. However, the relatively low representation of admixed populations in both GWAS summary statistics and individual-level datasets hinders the development of PRSs and equitable clinical translation for admixed populations. Prior work indicates that the most informative PRS model for a genetically homogeneous sample varies linearly in an ancestry continuum space. Guided by these observations, we introduce a genetic-distance-assisted PRS combination pipeline for diverse genetic ancestries (DiscoDivas) to interpolate a harmonized PRS for diverse, especially admixed, genetic ancestries. DiscoDivas leverages multiple PRS models fine-tuned within existing samples, which are mostly of single ancestry, and genetic distance. It provides a new approach to generate genetic-ancestry-specific PRSs when a suitably matched individual-level fine-tuning cohort is unavailable or underpowered. DiscoDivas treats genetic ancestry as a continuous variable and does not require shifting across different models when calculating PRSs for different ancestries. We generated PRSs with DiscoDivas and the current conventional method, i.e., fine-tuning multiple GWAS PRSs using the matched or similar genetic-ancestry samples. DiscoDivas generated a harmonized PRS, performing comparable to or better than the conventional approach, with the greatest advantage exhibited in admixed individuals.
SN  - 0002-9297
SN  - 1537-6605
ER  -